Mobility management in IP-Based Networks

Mobile communication networks experience a tremendous development clearly
evident from the wide variety of new applications way beyond classical
phone services. The tremendous success of the Internet along with the
demand for always-on connectivity has triggered the development of All-IP
mobile communication networks. Deploying these networks requires, however,
overcoming many challenges. One of the main challenges is how to manage the
mobility between cells connecting through an IP core in a way that
satisfies real-time requirements. This challenge is the focus of this
dissertation. This dissertation delivers an in-depth analysis of the
mobility management issue in IP-based mobile communication networks. The
advantages and disadvantages of various concepts for mobility management in
different layers of the TCP/IP protocol stack are investigated. In
addition, a classification and brief description of well-known mobility
approaches for each layer are provided. The analysis concludes that network
layer mobility management solutions seem to be best suited to satisfy the
requirements of future All-IP networks. The dissertation, therefore,
provides a comprehensive review of network layer mobility management
protocols along with a discussion of their pros and cons. Analyses of
previous work in this area show that the proposed techniques attempt to
improve the performance by making constraints either on access networks
(e.g. requiring a hierarchical topology, introducing of intermediate nodes,
etc.) or mobile terminals (e.g. undertaking many measurements, location
tracking, etc.). Therefore, a new technique is required that completes
handoffs quickly without affecting the end-to-end performance of ongoing
applications. In addition, it should place restrictions neither on access
networks nor on mobiles. To meet these requirements, a new solution named
Mobile IP Fast Authentication protocol (MIFA) is proposed. MIFA provides
seamless mobility and advances the state of the art. It utilizes the fact
that mobiles movements are limited to a small set of neighboring subnets.
Thus, contacting these neighbors and providing them in advance with
sufficient data related to the mobiles enable them to fast re-authenticate
the mobiles after the handoff. The dissertation specifies the proposal for
both IPv4 and IPv6. The specification of MIFA considers including many
error recovery mechanisms to cover the most likely failures. Security
considerations are studied carefully as well. MIFA does not make any
restrictions on the network topology. It makes use of layer 2 information
to optimize the performance and works well even if such information is not
available.In order to analyze our new proposal in comparison to a wide
range of well-known mobility management protocols, this dissertation
proposes a generic mathematical model that supports the evaluation of
figures such as average handoff latency, average number of dropped packets,
location update cost and packet delivery cost. The generic model considers
dropped control messages and takes different network topologies and
mobility scenarios into account. This dissertation also validates the
generic mathematical model by comparing its results to simulation results
as well as results of real testbeds under the same assumptions. The
validation proves that the generic model delivers an accurate evaluation of
the performance in low-loaded networks. The accuracy of the model remains
acceptable even under high loads. The validation also shows that simulation
results lie in a range of 23 %, while results of real testbeds lie in a
range of 30 % of the generic model?s results. To simplify the analysis
using the generic mathematical model, 4 new tools are developed in the
scope of this work. They automate the parameterization of mobility
protocols, network topologies and mobility scenarios. This dissertation
also evaluates the new proposal in comparison to well-known approaches
(e.g. Mobile IP, Handoff-Aware Wireless Access Internet Infrastructure
(HAWAII), etc.) by means of the generic mathematical model as well as
simulation studies modeled in the Network Simulator 2. The evaluation shows
that MIFA is a very fast protocol. It outperforms all studied protocols
with respect to the handoff latency and number of dropped packets per
handoff. MIFA is suitable for low as well as high speeds. Moreover, there
is no significant impact of the network topology on its performance. A main
advantage of MIFA is its robustness against the dropping of control
messages. It remains able to achieve seamless handoffs even if a dropping
occurs. The performance improvement is achieved, however, at the cost of
introducing new control messages mainly to distribute data concerning
mobile terminals to neighbor subnets. This results in more location update
cost than that resulting from the other mobility management protocols
studied. Due to excluding any constraints on the network topology, MIFA
generates the same packet delivery cost as Mobile IP and less than other
protocols.An additional focus of this dissertation is the development of an
adaptive eLearning environment that personalizes eLearning contents
conveying the topics of this dissertation depending on users?
characteristics. The goal is to allow researchers to quickly become
involved in research on mobility management, while learners such as
students are able to gain information on the topics without excess detail.
Analyses of existing eLearning environments show a lack of adaptivity
support. Existing environments focus mainly on adapting either the
navigation or the presentation of contents depending on one or more
selected users? characteristics. There is no environment that supports both
simultaneously. In addition, many user characteristics are disregarded
during the adaptivity process. Thus, there is a need to develop a new
adaptive eLearning environment able to eliminate these drawbacks. This
dissertation, therefore, designs a new Metadata-driven Adaptive eLearning
Environment (MAeLE). MAeLE generates personalized eLearning courses along
with building an adequate navigation at run-time. Adaptivity depends mainly
on providing contents with their describing metadata, which are stored in a
separate database, thus enabling reusing of eLearning contents. The
relation between the metadata that describe contents and those describing
learners are defined accurately, which enables a dynamic building of
personalized courses at run-time. A prototype for MAeLE is provided in this
dissertation as well.